Enhanced protection of 5G-IoT and beyond infrastructures: evolving intelligent strategies for DDoS attack multiclass classification

In the evolving landscape of next-generation networks beyond 5th Generation (5 G), the persistent threat of cyber-attacks remains a significant concern. 5G-IoT networks facilitate the deployment of numerous constrained and vulnerable IoT devices, making them attractive targets for hackers exploiting Distributed Denial of Service (DDoS) attacks (e.g., botnets), thereby increasing the attack surface. As a result, 5G infrastructures and service providers must develop robust systems for detecting and mitigating these threats. This research paper addresses these challenges by introducing a novel dataset collected from monitoring 5G-IoT multi-tenant traffic with multiple nested encapsulation headers. The dataset features six distinct network traffic classes tailored for Machine Learning (ML) model classification, offering a comprehensive understanding of network behaviour through aggregated features and metrics of 5G-IoT network flows across various topological scenarios. The HistGradBoost Classifier (HGBC) model excelled among the multiple ML models evaluated. It is known for its resilience in different network topology scenarios, effectively classifying network flows and enhancing defence mechanisms against potential attacks. The HGBC achieved F1-scores of 99.42 % and 98.62 % in the two scenarios presented in this study.